IC for universal computing with near zero programming complexity

ABSTRACT

A computing machine capable of performing multiple operations using a universal computing unit is provided. The universal computing unit maps an input signal to an output signal. The mapping is initiated using an instruction that includes the input signal, a weight matrix, and an activation function. Using the instruction, the universal computing unit may perform multiple operations using the same hardware configuration. The computation that is performed by the universal computing unit is determined by the weight matrix and activation function used. Accordingly, the universal computing unit does not require any programming to perform a type of computing operation because the type of operation is determined by the parameters of the instruction, specifically, the weight matrix and the activation function.

BACKGROUND OF THE INVENTION

The present invention generally relates to computing machines andIntegrated Circuits (ICs), and more specifically to a universalcomputing unit capable of performing multiple operations without programinstructions.

A goal of IC design methodologies is to provide both high performance inrelation to low power consumption and price, and high flexibility.However, traditional IC technologies, such as Applications SpecificIntegrated Circuits (ASICs) and Digital Signal Processors (DSPs), do notsatisfy both goals. An ASIC provides high performance with low powerconsumption and price, but provides very low flexibility. A DSP provideshigh flexibility, but provides low performance in relation to powerconsumption and price because a DSP requires extensive programmingcomplexity, control, and execution instructions to perform a completeapplication algorithm.

An IC typically performs multiple functions, such as addition,multiplication, filtering, Fourier transforms, and Viterbi decodingprocessing. Units designed with specific rigid hardware have beendeveloped to specifically solve one computation problem. For example,adder, multiplier, multiply accumulate (MAC), multiple MACs, FiniteImpulse Response (FIR) filtering, Fast Fourier Transform (FFT), andViterbi decoding units may be included in an IC. The adder unit performsadditional operations. The multiplier unit performs multiplicationoperations. The MAC unit performs multiplication and additionoperations. Multiple MACs can perform multiple multiplication andaddition operations. The FIR unit performs a basic filter computation.The FFT unit performs Fast Fourier Transform computations. And, theViterbi unit performs a maximum likelihood decoding processing.

The FIR, FFT, and Viterbi units are specially designed to performcomplicated filter, transform, and decoding computations. Multiple MACsmay be able to perform these operations, but performing the operationsrequires complicated software algorithms to complete a computation.Thus, performing the FIR filtering, FFT, and Viterbi decodingcomputations with multiple MACs requires an enormous amount ofprocessing time, which restricts the operations of the IC.

All of these units are implemented in rigid hardware to obtain the bestperformance of the specific operations. Thus, the functions performed bythe units may be performed faster by the IC because the IC includesunits to specifically perform certain operations. However, if anapplication does not need a provided operation, the hardware for theunused operation is wasted. For example, an IC may include FIR, FFT, andViterbi units. If an application does not need to perform a Viterbidecoding operation, the Viterbi unit is not used by the IC because theunit can only perform Viterbi operations. This results in dead siliconbecause the silicon used to implement Viterbi unit is wasted or not usedduring the execution of the application.

BRIEF SUMMARY OF THE INVENTION

In one embodiment of the present invention, a computing machine capableof performing multiple operations using a universal computing unit isprovided. The universal computing unit maps an input signal to an outputsignal. The mapping is initiated using an instruction that includes theinput signal, a weight matrix, and an activation function. Using theinstruction, the universal computing unit may perform multipleoperations using the same hardware configuration. The computation thatis performed by the universal computing unit is determined by the weightmatrix and activation function used. Accordingly, the universalcomputing unit does not require any programming to perform a type ofcomputing operation because the type of operation is determined by theparameters of the instruction, specifically, the weight matrix and theactivation function.

In one embodiment, the universal computing unit comprises a hardwarestructure that implements networked nodes that map an input signal to anoutput signal. The network connects nodes and the connections correspondto weights in the weight matrix. The input signal is mapped through theconnections in the networked nodes using the weights of the weightmatrix and the activation function to generate an output signal. Theoutput signal that is mapped is a result of the correspondingcomputation that is determined by the weight matrix and activationfunction.

With the specification of the weight matrix, and activation function,any operation may be performed by the universal computing unit. Theweight matrix and activation function used determine the operation thatis performed by the universal computing unit to generate the outputsignal that is being mapped.

In one embodiment, a computing unit in a computing machine is provided.The computing machine performs a plurality of computing operations usingthe computing unit. The computing unit comprising: a hardware structurethat implements networked nodes that receive an input signal and map theinput signal to an output signal, wherein nodes in the networked nodesare related by a network of connections between the nodes; a weightmatrix input that receives a weight matrix, wherein the weight matrixcomprises weights corresponding to the connections; and an activationfunction input that receives an activation function, wherein theactivation function specifies a function for the nodes in the network ofnodes, wherein the weight matrix and activation function correspond to acomputing operation, wherein the hardware structure maps the inputsignal though the network of connections in the networked nodes usingthe corresponding weights of the weight matrix for the connections andthe function of the activation function to generate the output signal,the output signal being a result of the computing operation that isdetermined by the weight matrix and activation function.

A further understanding of the major advantages of the invention hereinmay be realized by reference to the remaining portions of thespecification in the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a system for implementing anadaptable computing environment that includes a universal computing unit(UCU);

FIG. 2 illustrates an embodiment of the UCU;

FIG. 3 illustrates an example of a unity gain function and twonon-linear functions;

FIG. 4 illustrates an embodiment of networked nodes for the UCU;

FIG. 5 illustrates an embodiment of a weight matrix; and

FIG. 6 illustrates an embodiment of a hardware implementation of theUCU.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an embodiment of a computing machine 100 forimplementing an adaptable computing environment. Referring FIG. 1,computing machine 100 includes a switch 102. Switch 102 connects aninput data memory 104, registers 106, other computing units 108, auniversal computing unit 110, and a control memory 112. It will beunderstood that switch 102 is used for illustrative purposes and anymethod of connecting units together may be used. Switch 102 caninterconnect any of the units together. For example, switch 102 mayconnect all units together or may connect only specific units together.Typically, switch 102 receives a command indicating which units shouldbe connected together. For example, a command with binary valuescorresponding to the units may be sent to input data memory 104,registers 106, other computing units 108, universal computing unit 110,and control memory 112, where a value or routing coefficient, such as“1”, indicates that a unit should be switched on, and a value, such as“0”, indicates that a unit should not be switched on. The routingcoefficients replace a programming instruction stream by a datacoefficient stream. Thus, a traditional programming bus is made obsoleteby the use of routing coefficients and a traditional programminginstruction stream may be replaced with a data coefficient stream.Switch 102 allows the input data to be sent to the units andsubsequently receives the output data after processing by the units.

Computing machine 100 may be any Integrated Circuit (IC). Computingmachine 100 can perform a plurality of computing operations using aninstruction that is sent to UCU 110. The parameters of the instructiondetermine the type of computing operation that is performed by UCU 110.

In order to perform a computing operation, computing machine 100 may useany of the units shown in FIG. 1 and other units known in the art. Forexample, other computing units 108 may include adders, multipliers, andMACs to perform elementary computations. Examples of other uses are thatinput/data memory 104 and registers 106 may store data, such as an inputsignal or output signal, for UCU 110 and control memory 112 may storecontrol instructions, such as binary control codes. The control codesmay be for elementary computations and/or control parameters for UCU110.

FIG. 2 illustrates an embodiment of universal computing unit (UCU) 110.UCU 110 includes an input signal input to receive an input signal 202, aweight matrix input to receive a weight matrix 204, and an activationfunction to receive an activation function 206. Input signal 202, X, ismapped to output signal 204, Y, using weight matrix 206 and activationfunction 208. The matrix values and the selection of the activationfunction are coefficients that define the desired operation, which maybe called operation-coefficients.

Input signal 202 may be any signal that includes input data. Forexample, input signal 202 includes digital data such as a vector of onesand zeros. Universal computing unit 110 maps input data to output datausing weight matrix 206 and activation function 208.

Weight matrix 206 is a matrix of weights. In one embodiment, weightmatrix 206 is a matrix of n×m dimensions. Weight matrix 206 includescoefficients that are used in calculations with input data. Weightmatrix 206 will be described in more detail hereinafter.

Activation function 208 is a function applied to a result of acalculation at a node. Each node or groups of nodes of UCU 110 may havean associated activation function or a one activation function may beassociated with every node. In one embodiment, activation function 208may be of two types. The first type is a linear function, such as aunity gain function, which is mainly used for linear processingalgorithms. The second function is a nonlinear function, such as asigmoid or limiter function, which is mainly used for nonlinearprocessing algorithms.

FIG. 3 illustrates an example of a unity gain function 300, a sigmoidfunction 302 and a limiter function 304. As shown, unity gain function300 is a linear function where output increases and decreases linearlywith input. Sigmoid function 302 is a nonlinear function where outputincreases and decreases non-linearly with input. Limiter function 304 isa nonlinear function output increases and decreases non-linearly withinput. Other non-linear functions known in the art may also be used asactivation function 208.

In one embodiment, UCU 110 includes a hardware structure that implementsone or more nodes connected by a network that map input signal 202 tooutput signal 204 using weight matrix 206 and activation function 208.In one embodiment, the nodes may be organized in layers and form amulti-layer perceptron network. For example, a three layer network isused to map input signal 202 to output signal 204. In one embodiment,multi-layer perceptron networks may be used as described in “AppliedNeural Networks for Signal Processing”, Fa-Long Luo and Rolf Unbehauen,University Press, 2000, which is herein incorporated by reference forall purposes. Although three layers are used for discussion purposes, itwill be understood that any number of layers may be used in the network.

FIG. 4 illustrates an embodiment of networked nodes 400 for UCU 110. Asshown, networked nodes 400 includes three layers. First layer 402receives input signal 202 in the form of a vector of N dimensions,X=[X₁, X₂, X₃, . . . , X_(N)]. In one embodiment, networked nodes 400operates as a multi-layer perceptron network. Each layer may include anynumber of nodes. For example, the nodes of first layer 402 arerepresented by 1-N, the nodes of second layer 404 are represented by1−L, and the nodes of third layer 406 are represented by 1-M.

As shown, networked nodes 400 includes connections between each layer.Data flows through the connections of networked nodes 400 from left toright. The connections are represented as W_(nx) ^((i)), where “x” isthe index of the node at the ending point (right side) of theconnection, “n” is the index of the node at the source point (left side)of the connection, and “i” is the index for the related layers using thecorresponding source layer. The connections are shown connecting firstlayer 402 and second layer 404, and the second layer 404 and third layer406. However, nodes may be connected in other ways.

Each connection between layers has a corresponding weight coefficient inweight matrix 206. FIG. 5 illustrates an embodiment of weight matrix206, W that may be used for networked nodes 400. Weight matrix 206includes two sub-matrices W₁ and W₂. W₁ is the weight matrix forconnections between first layer 402 and second layer 404; and W₂ is theweight matrix for connections between second layer 404 and third layer406. Any number of sub-matrices may be used and additional sub-matricesmay be used if additional layers are included in networked nodes 400. Asshown, each weight corresponds to a connection in networked nodes 400.For example, weight W₁₂ ⁽¹⁾ in matrix W₁ is the weight for theconnection between the second node of second layer 404 and the firstnode of first layer 402. In one embodiment, the connections for a nodeare found by taking a column of one of the matrices. For example, thefirst column of matrix W₁ includes the connections for the first node ofsecond layer 404, the second column for the second node of second layer404, etc.

Referring back to FIG. 4, the N dimensions of input signal 202 are fedinto the nodes of first layer 402 and the values of second layer 404 arethen processed. In one embodiment, the value of a node in a layer is thedot product of the weights of the connections to the node and thecorresponding values of the connected nodes in the prior layer. Thus,the dot product of each node of second layer 404 is determined by thedot product of the weights of the connections and the correspondingvalues of the connected nodes in first layer 402. In this example, thedot product of the nodes of second layer 404 may be represented as:${X^{(1)}(j)} = {\sum\limits_{i = 1}^{N}\quad{W_{ij}^{(1)}{X_{i}.}}}$

X^((i)) (j) is the dot product of all connections to the j'th node insecond layer 404. W_(ij) ^((i)) represents the weights for theconnections to the j'th node of second layer 404, and X_(i) representsthe values of the connected nodes.

Once the dot product of the connections is determined, the activationfunction is applied to the result to produce the output of the node. Ifthe activation function is represented as F( ), the output of the nodemay be represented as:${Y^{(1)}(j)} = {{F\left( {\sum\limits_{i = 1}^{N}\quad{W_{ij}^{(1)}X_{i}}} \right)}.}$

The output of the node is then used in the processing between secondlayer 404 and third layer 406. The processing is similar to second layer404 processing but third layer 406 processing uses the matrix W₂.

The nodes in third layer 406 perform the computation of:${X^{(2)}(j)} = {\sum\limits_{i = 1}^{L}\quad{W_{ij}^{(2)}{{Y^{(1)}(i)}.}}}$

X⁽²⁾ (j) is the dot product of all connections to the j'th nodes inthird layer 406. W_(ij) ⁽²⁾ represents the weights for the connectionsfor the j'th nodes of third layer 406, and Y⁽¹⁾ (i) represents thevalues of the connected nodes originating from second layer 404.

Once the dot products of the connections are determined, the activationfunction is applied to the result to produce the output of the node. Ifthe activation function is represented as F( ), the output of the nodemay be represented as:${Y^{(2)}(j)} = {{F\left( {\sum\limits_{i = 1}^{L}\quad{W_{ij}^{(2)}{Y^{(1)}(i)}}} \right)}.}$

The output Y_(j) (at the j'th node) of third layer 406 then constitutesoutput signal 204, which may be represented as:$Y_{j} = {{Y^{(2)}(j)} = {F\left( {\sum\limits_{i = 1}^{L}\quad{W_{ij}^{(2)}{Y^{(1)}(i)}}} \right)}}$

UCU 110 is configured to perform multiple computations by receiving asingle instruction. The single instruction may be represented asY=UCU(X, W, S), where Y is output signal 204, X is input signal 202, Wis weight matrix 206, and S is the type of the activation function 208.Once UCU 110 receives parameters X, W, and S, the output is mapped byUCU 110. The mapped output is the result of a specific computation, suchas Discrete Fourier Transforms (DFTs), FIR filtering, or Viterbidecoding processing. However, the type of computation is not explicitlyspecified to UCU 110. Rather, the type of computation performed by UCU110 is controlled by the parameters W and S that are included in theinstruction. Weight matrix 206 is configured with different coefficientsfor different computations. Thus, different computations may beperformed by UCU 110 by changing the weights of weight matrix 206 andactivation function 208. No programming is required to changeoperations, data is fed through UCU 110 and the values of weight matrix206 and activation function 208 determine the output of UCU 110. Thus,the specific computation associated with weight matrix 206 andactivation function 208 is performed by mapping. Accordingly, UCU 110 isadaptable to perform multiple operations using the same instruction withdifferent weights and activation functions as parameters. Alternatively,UCU 110 may receive an instruction including the parameters W and S anduse the parameters to map input signals or an input stream to outputsignals or an output stream.

Examples of different operations that may be performed by UCU 110 willnow be described. Although the following operations are described, aperson skilled in the art will understand that UCU 110 may perform anydesired linear or non-linear operation by mapping input data to outputdata.

According to definition, the DFT of an input signal X is: Y=FX, where Fis a known transform matrix. The instruction, Y=UCU (X, W, S), is usedto perform a DFT computation using UCU 110. Weight matrix 206 isrepresented by the known transform matrix, F, as a weight matrix, W₁,between first layer 402 and second layer 404 and an identity matrix, I,as the weight matrix, W₂, between second layer 404 and third layer 406.An identity matrix is a matrix whose diagonal elements are unity and therest are zeros. The activation function is also a unity gain functionand represented by S=0. Accordingly, the instruction sent to UCU 110 toperform a DFT function is: Y=UCU (X, [F, I], 0). Using the instruction,UCU 110 performs a DFT computation by mapping input signal X throughconnections between networked nodes 400 to generate the desired outputsignal Y.

UCU 110 may also perform FIR filtering computations. By definition, theFIR filter output of an input signal X is:${{y(n)} = {\sum\limits_{m = 0}^{I}\quad{{a(m)}{x\left( {n - m} \right)}}}},$where x(n−m), y(n), and a(m) are the input, output, and filtercoefficients, respectively. This FIR processing may be performed by UCU110 using the instruction: Y=UCU (X, W, S)=UCU (X, [A, I], 0), where Ais a matrix comprising the filter coefficients, X is the input vector,and Y is the output vector. The matrix, W₁, between first layer 402 andsecond layer 404 is A. The matrix, W₂, between second layer 404 andthird layer 406 is the identity matrix. The activation function (S=0) isthe unity gain function. Using the above instruction, UCU 110 performsan FIR filtering for input signal X to produce output signal Y. Theinput signal is mapped through connections in networked nodes 400 usingthe weight matrix and activation function to generate the output signal.

UCU 110 may also perform nonlinear computations. For example, patternclassifications expressed as Y=G(X) are performed. The function G(X) isapproximated by UCU 110 by mapping input signals to output signals. Inorder to perform a nonlinear computation, activation function 208 is setto a nonlinear setting (S=1), and a sigmoid function is used. Thus, theinstruction Y=UCU(X, W, 1) is used to perform pattern classifications.

In one embodiment, the weight matrix W may be determined by offlinelearning algorithms that approximate the above mapping of the functionG( ). To determine weight matrix W, a training stage or preprocessingstage is performed where weights are set to produce the desired output.For example, an input is fed into networked nodes 400 with an initialweight matrix of weights. Then, it is determined if the output ofnetworked nodes 400 is the desired mapping of the input signal for thepattern classification. If so, the weights of weight matrix W areacceptable. This process is repeated for multiple inputs and the weightsare adjusted until all inputs are mapped to their desired outputs with asubstantial degree of accuracy. The weights of the final weight matrixare used in weight matrix W for the specific pattern classification.Once the weights are set for a classification, the classification isperformed by using the above instruction with the weight matrix W thatwas determined in the learning phase of the preprocessing.

Using the instruction Y=UCU(X, W, 1), with the determined weight matrixW for the pattern classification that is to be performed, UCU 110 mapsan input signal X to the desired output signal Y. Thus, any non-linearfunction may be mapped using UCU 110. The desired output signal for aninput signal is mapped through connections of networked nodes 400 usingthe weight matrix and activation function.

FIG. 6 illustrates an embodiment of a hardware implementation 600 of UCU110 that implements networked nodes 400 for mapping input signal 202 tooutput signal 204. Hardware implementation 600 includes a first layer,second layer, and third layer. The first, second, and third layerscorrespond to first layer 402, second layer 404, and third layer 406 ofFIG. 4, respectively.

Hardware implementation 600 also includes a weight matrix module 622 andactivation function (AF) control module 620. Weight matrix module 622includes one or more weight matrices. The weight matrices correspond tothe different computations that UCU 110 may perform. Weight matrixmodule 622 is configured to send the appropriate weights to nodes in thesecond and third layers.

AF control module 620 includes one or more activation functions. AFcontrol module 620 is configured to send a command to nodes in thesecond and third layers indicating the type of activation function toapply.

The first layer includes a multiplexer (MUX) 602. MUX 602 receives inputsignal 202 of N dimensions and sends the appropriate values, X₁ . . .X_(N), of input signal 202 to modules 604 of the second layer. Theappropriate vector values are determined by the connections betweennodes as shown in FIG. 4. For example, every node in second layer 404receives all the values of the nodes in first layer 402. Thus, MUX 602sends every vector value of input signal 202 to each module 604.Although a multiplexer is used as the first layer, a person skilled inthe art will recognize other ways of implementing a first layer.

The second layer includes one or more second layer modules 604. A module604 includes, in one embodiment, a multiply-accumulate unit (MAC) 606and an activation function unit (AF) 608. Each MAC 606 (the index is“j”) performs the computation of:${{X^{(1)}(j)} = {\sum\limits_{i = 1}^{N}\quad{W_{ij}^{(1)}X_{i}}}},$where j is the index of MAC 606 for this layer.

Each MAC 606 receives values of input signal 202 and the correspondingweights from weight matrix module 622 for the connections. Thecomputation is then performed and passed to AF 608. AF control 620provides an instruction, such as a “0” or “1” to each AF 608 thatdetermines whether a unity gain function or sigmoid function should beapplied by AF 608. AF 608 (the corresponding index is “j”) then performsthe computation of:${{Y^{(1)}(j)} = {{F\left( {X^{(1)}(j)} \right)} = {F\left( {\sum\limits_{i = 1}^{N}\quad{W_{ij}^{(1)}X_{i}}} \right)}}},$

as described above. If S=0, the above equation may be simplified to:${Y^{(1)}(j)} = {{X^{(1)}(j)} = {\sum\limits_{i = 1}^{N}\quad{W_{ij}^{(1)}{X_{i}.}}}}$

Each second layer module 604 corresponds to a node in second layer 404as described in FIG. 4. Although one or more second layer modules 604are used as the second layer, a person skilled in the art will recognizeother ways of implementing a second layer. For example, any number ofMAC 606 and AF 608 units may be used. Additionally, a structureincluding a single multiply-accumulate unit, such as an FIR filter,combined with an activation function unit, such as AF 608, may be usedto implement the second layer. However, if these structures are used,the computation may take longer because the structures do not include aseparate unit for each node. Thus, the computation for each node has tobe cycled through the structure multiple times using softwarealgorithms.

The third layer includes a MUX 610 and one or more third layer modules612. Additionally, a MUX 614 may be included for sending output signal204. Similarly to second layer modules 604, a third layer module 612will also include a multiply-accumulate unit, MAC 616, and an activationfunction unit, AF 618. The third layer operates in a similar manner asthe second layer. The resulting values from the second layer are sent toMUX 610, which then sends the appropriate values to third layer modules612 based on the connections shown between second layer 404 and thirdlayer 406 in FIG. 4. Third layer modules 612 also receive weights fromweight matrix module 622. The weight matrix is typically the matrix forthe connections between the second and third layer. Also, an activationfunction from AF 620 is received.

The computations in third layer modules 612 proceeds as described abovewith regards to second layer modules 604. Each MAC 616 performs thecomputation of:${{X^{(2)}(j)} = {\sum\limits_{i = 1}^{L}\quad{W_{ij}^{(2)}{Y^{(1)}(i)}}}},$

where “j” is the index of MAC 616 in this layer. Each MAC 616 receivesvalues Y⁽¹⁾ (i) from the second layer through MUX 610 and thecorresponding weights W_(ij) ⁽²⁾ from weight matrix module 622. Thecomputation is then performed in MAC 616 and passed to AF 618. AFcontrol 620 provides an instruction, such as a “0” or “1” to each AF 618that determines whether a unity gain function or sigmoid function shouldbe applied by AF 618. AF 618 performs the computation of:${Y_{j} = {{Y^{(2)}(j)} = {{F\left( {X^{(2)}(j)} \right)} = {F\left( {\sum\limits_{i = 1}^{L}\quad{W_{ij}^{(2)}{Y^{(1)}(i)}}} \right)}}}},$

as described above.

Each module 612 corresponds to a node in third layer 406 of FIG. 4.Although one or more third layer modules 612 are used as the thirdlayer, a person of skill in the art will appreciate other ways ofimplementing a third layer. For example, similar to the second layer,any number of MAC 616 and AF 618 units may be used. Additionally, astructure including a single multiply-accumulate unit, such as an FIRfilter, combined with activation function unit, such as AF 618, may beused to implement the third layer. However, if these structures areused, the computation may take longer because the structures do notinclude a separate unit for each node. Thus, the computation for eachnode has to be cycled through the structure multiple times usingsoftware algorithms. Additionally, in another embodiment, the samemodule used in the second layer may be used in the third layer.

The output of third layer modules 612 is sent to MUX 614, which outputsthe mapped output signal 204. Thus, input signal 202 has been mapped tooutput signal 204 using hardware implementation 600. Although MUX 614 isused for outputting output signal 204, a person of skill in the art willappreciate other ways of outputting output signal 204. For example,output signal 204 may be directly passed from third layer modules 612.Additionally, other hardware implementations may be used to implementUCU 110. For example, any hardware structure that can implementnetworked nodes 400 and map an input signal to an output signal usingweight matrix 206 and activation function 208 may be used.

Accordingly, computing machine 100 can perform a plurality of computingoperations using single instruction that is sent to UCU 110. Typically,computing operations, such as DFT, FIR filtering, and patternclassifications computations, require multiple programming instructionsto perform a computation. However, UCU 110 requires the specification ofoperation-coefficients to map input data to output data, where theoutput data is a result of a computing operation defined by theoperation-coefficients. Thus, the operations-coefficients replace aprogramming instruction stream with a data coefficient instructionstream. The parameters of the instruction determine the type ofcomputing operation that is performed by UCU 110. Thus, universalcomputing unit 110 does not require programming instructions to performdifferent types of computing operation because the type of operation iscontrolled by the weight matrix and activation function. Programminginstructions are replaced by the weight matrix and an instruction set issimplified to a “stop” and “go” instruction for UCU 110. The parametersof the weight matrix and activation are specified and input data isstreamed through UCU 110 to produce output data. Thus, a programming busis not needed and becomes obsolete.

The above description is illustrative but not restrictive. Manyvariations of the invention will become apparent to those skilled in theart upon review of this disclosure. The scope of the invention should,therefore, be determined not with reference to the above description,but instead should be determined with reference to the pending claimsalong with their full scope or equivalents.

1. A computing unit in a computing machine, wherein the computingmachine performs a plurality of computing operations using the computingunit, the computing unit comprising: a hardware structure thatimplements networked nodes that receive an input signal and map theinput signal to an output signal, wherein nodes in the networked nodesare related by a network of connections between the nodes; a weightmatrix input that receives a weight matrix, wherein the weight matrixcomprises weights corresponding to the connections; and an activationfunction input that receives an activation function, wherein theactivation function specifies a function for the nodes in the network ofnodes, wherein the weight matrix and activation function correspond to acomputing operation, wherein the hardware structure maps the inputsignal though the network of connections in the networked nodes usingthe corresponding weights of the weight matrix for the connections andthe function of the activation function to generate the output signal,the output signal being a result of the computing operation that isdetermined by the weight matrix and activation function.
 2. Thecomputing unit of claim 1, wherein the networked nodes are arranged in aplurality of layers.
 3. The computing unit of claim 1, wherein thenetworked nodes form a multi-layer perceptron network.
 4. The computingunit of claim 1, wherein the weight matrix comprises a plurality ofsub-matrices.
 5. The computing unit of claim 1, wherein the function ofthe activation function comprises a linear function.
 6. The computingunit of claim 5, wherein the linear function comprises a unity gainfunction.
 7. The computing unit of claim 1, wherein the function of theactivation function comprises a nonlinear function.
 8. The computingunit of claim 7, wherein the nonlinear function comprises a sigmoidfunction.
 9. The computing unit of claim 7, wherein the nonlinearfunction comprises a limiter function.
 10. The computing unit of claim1, wherein the computing machine comprises an integrated circuit. 11.The computing unit of claim 1, wherein the hardware structure comprisesone or more units capable of performing multiplication and accumulationoperations and one or more activation function units.
 12. A computingunit in a computing machine, wherein the computing machine performs aplurality of computing operations using the computing unit, thecomputing unit comprising: an input layer of nodes for receiving aninput signal; a middle layer of nodes coupled to the input layer ofnodes, wherein the middle layer of nodes are related to the input layerof nodes through a first network of connections, the middle layerconfigured to process the input signal using middle layer weightscorresponding to the first network of connections and an activationfunction to generate a middle layer signal; and an output layer of nodescoupled to the middle layer of nodes, wherein the output layer of nodesare related to the middle layer of nodes through a second network ofconnections, the output layer configured to process the middle layersignal using output layer weights corresponding to the second network ofconnections and the activation function to generate an output signal,the output signal being a result of a computing operation correspondingto the middle and output layer weights and the activation function. 13.The computing unit of claim 12, wherein the input, middle, and outputlayers are constructed into a multi-layer perceptron network.
 14. Thecomputing unit of claim 12, wherein the input layer of nodes is amultiplexer.
 15. The computing unit of claim 12, wherein a node in themiddle layer of nodes comprises one or more units capable of performingmultiply and accumulate operations and one or more activation functionunits.
 16. The computing unit of claim 15, wherein one or more unitscapable of performing multiply and accumulate operations comprisemultiply-accumulate units.
 17. The computing unit of claim 12, wherein anode in the output layer of nodes comprises one or more units capable ofperforming multiplication and accumulation operations and one or moreactivation function units.
 18. The computing unit of claim 17, whereinone or more units capable of performing multiplication and accumulationoperations comprise multiply-accumulate units.
 19. The computing unit ofclaim 12, wherein the activation function comprises a linear function.20. The computing unit of claim 19, wherein the linear functioncomprises a unity gain function.
 21. The computing unit of claim 12,wherein the activation function comprises a nonlinear function.
 22. Thecomputing unit of claim 21, wherein the nonlinear function comprises asigmoid function.
 23. The computing unit of claim 21, wherein thenonlinear function comprises a limiter function.
 24. The computing unitof claim 12, further comprising a weight matrix, wherein the weightmatrix comprises the middle layer and output layer weights.
 25. Thecomputing unit of claim 12, wherein a node in the middle layer isconfigured to process the input signal using middle layer weights bycomputing a dot product of the middle layer weights and input signal forthe connection to the node.
 26. The computing unit of claim 25, whereinthe node in the middle layer is configured to process the dot product byapplying the activation function to the dot product.
 27. The computingunit of claim 12, wherein a node in the output layer is configured toprocess the middle layer signal using the output layer weights bycomputing a dot product of the output layer weights and middle layersignal for the connections to the node.
 28. The computing unit of claim27, wherein the node in the output layer is configured to process thedot product by applying the activation function to the dot product. 29.The computing unit of claim 12, wherein the weights determine theconnection of nodes.
 30. The computing unit of claim 12, wherein thecomputing machine comprises an integrated circuit.
 31. A method forperforming a plurality of computing operations with a computing unitusing a weight matrix and an activation function, the computing unitcomprising a hardware structure that implements networked nodes, whereinnodes in the networked nodes are related by a network of connectionsbetween the nodes, wherein the weight matrix comprises weightscorresponding to the connections and the activation function specifies afunction for the nodes in the networked nodes, the method comprising:receiving an instruction that is applied to an input signal at thecomputing unit, wherein the instruction includes the weight matrix andthe activation function, the weight matrix and activation functioncorresponding to a computing operation; and mapping the input signalthrough the network of connections in the networked nodes using thecorresponding weights of the weight matrix for the connections andfunction of the activation function for the nodes to generate an outputsignal, wherein the output signal is a result of the computing operationdetermined by the weight matrix and activation function.
 32. The methodof claim 31, wherein the networked nodes form a multi-layer perceptronnetwork.
 33. The method of claim 32, wherein the multi-layer perceptronnetwork is a three layer perceptron network.
 34. The method of claim 32,wherein mapping the input signal through the network of connections inthe networked nodes using the corresponding weights of the weight matrixfor the connections comprises computing a dot product for a node,wherein the dot product is a computation of values of nodes connected tothe node and the corresponding weights for the connections to the node.35. A method for performing a plurality of computing operations with acomputing unit using a weight matrix and an activation function, thecomputing unit comprising a hardware structure that implements networkednodes, wherein nodes in the networked nodes are related by a network ofconnections between the nodes, wherein the weight matrix comprisesweights corresponding to the connections and the activation functionspecifies a function for the nodes in the networked nodes, the methodcomprising: receiving an input signal at an input layer in the networkednodes; sending the input signal to one or more nodes in a middle layerthat are related by connections with the input layer; receiving middlelayer weights for the connections between the input layer and middlelayer from the weight matrix; processing the input signal using themiddle layer weights and the function of the activation function togenerate a middle layer signal; sending the middle layer signal to oneor more nodes in an output layer that are related by connections withthe middle layer; receiving output layer weights for the connectionsbetween the middle layer and output layer from the weight matrix; andgenerating an output signal by processing the middle layer signal usingthe weights and the function of the activation function.
 36. The methodof claim 35, wherein processing the input signal using the middle layerweights comprises computing a dot product for a node, wherein the dotproduct is between values of nodes connected to the node and middlelayer weights for the connections to the node.
 37. The method of claim36, wherein processing the input signal using the function of theactivation function comprises computing the function of the dot product.38. The method of claim 35, wherein processing the middle layer signalusing the middle layer weights comprises computing a dot product for anode, wherein the dot product is between the middle layer signal andoutput layer weights for the connections to the node.
 39. The method ofclaim 38, wherein processing the middle layer signal using the functionof the activation function comprises computing the function of dotproduct.
 40. A universal computing unit in a computing machine, whereinthe computing machine maps an input signal to an output signal using theuniversal computing unit, the universal computing unit comprising: afirst layer configured to receive the input signal; a second layercoupled to the first layer, the second layer comprising one or moremultiply-accumulate (MAC) units and one or more activation functionmodules, wherein the one or more MAC units are configured to receive theinput signal and second layer weights from a weight matrix and calculateone or more dot products of the received second layer weights and inputsignal, wherein the one or more activation function modules areconfigured to calculate a function of the one or more dot products ofthe received second layer weights and input signal to generate a secondlayer signal; and a third layer coupled to the second layer, the thirdlayer comprising one or more MAC units and one or more activationfunction modules, wherein the one or more MAC units are configured toreceive the second layer signal and third layer weights from the weightmatrix and calculate one or more dot product of the received third layerweights and second layer signal, wherein the one or more activationfunction modules are configured to calculate a function of the one ormore dot products of the received third layer weights and second layersignal to generate the output signal.
 41. The universal computing unitof claim 40, further comprising a weight matrix module configured tosend the second and third layer weights to the one or more MAC units ofthe second and third layers.
 42. The universal computing unit of claim40, further comprising an activation function module configured to sendthe function to the one or more activation function modules.
 43. Theuniversal computing unit of claim 40, wherein the first layer comprisesa multiplexer.
 44. The universal computing unit of claim 40, wherein thesecond layer comprises a multiplexer configured to receive the secondlayer signal and send the second layer signal to the one or more MACs ofthe third layer.
 45. The universal computing unit of claim 40, whereinthe third layer comprises a multiplexer configured to provide the outputsignal.
 46. A method for performing a plurality of computing operationswith one or more universal computing units, the one or more universalcomputing units being part of a network that couples the one or moreuniversal computing units to one or more computing units, the methodcomprising: receiving routing coefficients that specify connectivityinformation for the one or more universal computing units and one ormore computing units in the network, wherein the routing coefficientsreplace a programming instruction stream by a data coefficient stream;connecting the one or more universal computing units and one or morecomputing units in the network based on the routing coefficients;receiving an instruction through the connected network comprising aweight matrix and a selection of an activation function, wherein theweight matrix and selection of the activation function comprise a set ofoperation-coefficients that define a desired computing operation in theplurality of computing operations; receiving an input data streamthrough the connected network; and mapping an output data stream for theinput data stream using the connected one or more universal computingunits and one or more computing units and the set ofoperation-coefficients, the output data stream being a result of thedefined desired computing operation.